{"id":"W1834099830","doi":"10.3141/2233-03","title":"Corridor-Level Air Quality Analysis of Freight Movement","year":2011,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Commission for Environmental Cooperation; U.S. Environmental Protection Agency","keywords":"Truck; Air quality index; Fuel efficiency; Transport engineering; Environmental science; Rail freight transport; Vehicle miles of travel; Air pollution; Climate change; Greenhouse gas; Engineering; Meteorology; Automotive engineering; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004217615,0.0002077618,0.0005786159,0.001555107,0.000273552,0.0000243935,0.000945596,0.0001591547,0.001196597],"category_scores_gemma":[0.00005950598,0.0001543322,0.0005663726,0.00395515,0.0003573658,0.0004370278,0.000005561877,0.001457851,0.00001043287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001550006,"about_ca_system_score_gemma":0.0002380099,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008179839,"about_ca_topic_score_gemma":0.02548921,"domain_scores_codex":[0.9941581,0.0005214597,0.001665763,0.000242382,0.002771037,0.0006412123],"domain_scores_gemma":[0.9960304,0.0004261245,0.0003285069,0.0005445071,0.002330872,0.0003395991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001186605,0.000457121,0.917316,0.0005910418,0.002111616,0.0000543789,0.009076786,0.0350378,0.01526021,0.003945204,0.003978895,0.01098439],"study_design_scores_gemma":[0.0006699405,0.0002372084,0.9798157,0.0001500018,0.0001883955,7.812268e-8,0.001073653,0.002352095,0.0124364,0.001044592,0.001872728,0.0001592132],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929247,0.0001954268,0.004825912,0.0002075537,0.0003394815,0.0003970638,0.0002048934,0.00002923651,0.0008757532],"genre_scores_gemma":[0.9958327,0.001037542,0.002565677,0.00002290249,0.00006222872,0.00002897719,0.00002116656,0.00003732718,0.0003915128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06249975,"threshold_uncertainty_score":0.9997165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1897117874725573,"score_gpt":0.3820837339656784,"score_spread":0.192371946493121,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}